While there are some other data augmentation methods such as adding noise or randomly dropout [1]}, [2]}, the aforementioned random-mixing data augmentation, albeit simple, has been shown particularly successful [3]}. However, when mixing two tracks, there are actually multiple aspects to consider [4]}, suggesting room for the development of more advanced mixing-specific data augmentation methods for source separation. To our best knowledge, this has not yet been investigated in the literature. It is therefore our goal to develop, and to empirically evaluate the effectiveness of, new data augmentation methods that stem from the random-mixing approach. We consider in total three types of mixing-specific data augmentation methods for source separation, as conceptually visualized in Figure REF  and detailed in Section .
